Creating a realtime RAG voice agent
Blog post from Cerebrium
This tutorial demonstrates how to create a real-time RAG (Reactive Audio Generation) voice agent using Cerebrium, leveraging external APIs for improved performance and scalability. The project utilizes Daily's Deepgram model locally for fast STT conversion, ElevenLabs for voice cloning, OpenAI's GPT-4o-mini model for LLM-based retrieval, and Pinecone as the vector store. The application allows users to ask questions about video lectures and receive personalized explanations in Andrej Karpathy's original voice. By combining RAG with voice capabilities, this project unlocks various applications and enables customization through trade-offs between latency, cost, and accuracy.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| Vector Search | 9 | 1,704 | 240 | 102 | -4% |
| LLM | 4 | 4,537 | 421 | 147 | +51% |
| RAG | 4 | 1,801 | 200 | 85 | +50% |
| Voice AI | 3 | 145 | 46 | 21 | -37% |
| Real-time | 1 | 2,310 | 734 | 231 | -11% |
| Secrets Management | 1 | 616 | 101 | 53 | -49% |
Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.